To address the aforementioned limitations, in this work, we borrow the graph diffusion technique [1]}, [2]} to sample a fixed-length and importance-aware contextual node set for each target edge. With graph diffusion, a global view of graph structure is acquired, and then we can quantify the importance of each node for a given target node/edge. Formally, given an adjacency matrix of a static graph \(\mathbf {A} \in \mathbb {R}^{n \times n}\) , the definition of graph diffusion \(\mathbf {S} \in \mathbb {R}^{n \times n}\)  is given as
\(\mathbf {S}=\sum _{k=0}^{\infty } \theta _{k} \mathbf {T}^{k},\) 
